Overview

Dataset statistics

Number of variables23
Number of observations182374
Missing cells32476
Missing cells (%)0.8%
Duplicate rows128
Duplicate rows (%)0.1%
Total size in memory33.4 MiB
Average record size in memory192.0 B

Variable types

Categorical11
DateTime2
Text6
Numeric4

Alerts

Year has constant value "2023-24"Constant
Channel-2 has constant value "LFS"Constant
Billing grp has constant value "LFS"Constant
Dataset has 128 (0.1%) duplicate rowsDuplicates
Brand is highly imbalanced (92.1%)Imbalance
Region has 14357 (7.9%) missing valuesMissing
Franchisee store has 18119 (9.9%) missing valuesMissing
Quantity is highly skewed (γ1 = 24.45627323)Skewed

Reproduction

Analysis started2024-05-21 04:45:45.762350
Analysis finished2024-05-21 04:46:04.056272
Duration18.29 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2023-24
182374 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1276618
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-24
2nd row2023-24
3rd row2023-24
4th row2023-24
5th row2023-24

Common Values

ValueCountFrequency (%)
2023-24 182374
100.0%

Length

2024-05-21T10:16:04.355368image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:04.650118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2023-24 182374
100.0%

Most occurring characters

ValueCountFrequency (%)
2 547122
42.9%
0 182374
 
14.3%
3 182374
 
14.3%
- 182374
 
14.3%
4 182374
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1276618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 547122
42.9%
0 182374
 
14.3%
3 182374
 
14.3%
- 182374
 
14.3%
4 182374
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1276618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 547122
42.9%
0 182374
 
14.3%
3 182374
 
14.3%
- 182374
 
14.3%
4 182374
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1276618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 547122
42.9%
0 182374
 
14.3%
3 182374
 
14.3%
- 182374
 
14.3%
4 182374
 
14.3%

Month
Date

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2023-02-01 00:00:00
Maximum2024-01-01 00:00:00
2024-05-21T10:16:04.926181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:16:05.316122image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)

Month Key
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Dec
20659 
Jul
18119 
Aug
17510 
May
17340 
Jun
17298 
Other values (6)
91448 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters547122
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr
2nd rowApr
3rd rowApr
4th rowApr
5th rowApr

Common Values

ValueCountFrequency (%)
Dec 20659
11.3%
Jul 18119
9.9%
Aug 17510
9.6%
May 17340
9.5%
Jun 17298
9.5%
Oct 16884
9.3%
Apr 16354
9.0%
Nov 16164
8.9%
Jan 14694
8.1%
Sep 14085
7.7%

Length

2024-05-21T10:16:05.666298image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec 20659
11.3%
jul 18119
9.9%
aug 17510
9.6%
may 17340
9.5%
jun 17298
9.5%
oct 16884
9.3%
apr 16354
9.0%
nov 16164
8.9%
jan 14694
8.1%
sep 14085
7.7%

Most occurring characters

ValueCountFrequency (%)
u 52927
 
9.7%
J 50111
 
9.2%
e 48011
 
8.8%
c 37543
 
6.9%
A 33864
 
6.2%
a 32034
 
5.9%
n 31992
 
5.8%
p 30439
 
5.6%
D 20659
 
3.8%
l 18119
 
3.3%
Other values (12) 191423
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 52927
 
9.7%
J 50111
 
9.2%
e 48011
 
8.8%
c 37543
 
6.9%
A 33864
 
6.2%
a 32034
 
5.9%
n 31992
 
5.8%
p 30439
 
5.6%
D 20659
 
3.8%
l 18119
 
3.3%
Other values (12) 191423
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 52927
 
9.7%
J 50111
 
9.2%
e 48011
 
8.8%
c 37543
 
6.9%
A 33864
 
6.2%
a 32034
 
5.9%
n 31992
 
5.8%
p 30439
 
5.6%
D 20659
 
3.8%
l 18119
 
3.3%
Other values (12) 191423
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 52927
 
9.7%
J 50111
 
9.2%
e 48011
 
8.8%
c 37543
 
6.9%
A 33864
 
6.2%
a 32034
 
5.9%
n 31992
 
5.8%
p 30439
 
5.6%
D 20659
 
3.8%
l 18119
 
3.3%
Other values (12) 191423
35.0%

QTR
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Q3
53707 
Q1
50992 
Q2
49714 
Q4
27961 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters364748
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q3 53707
29.4%
Q1 50992
28.0%
Q2 49714
27.3%
Q4 27961
15.3%

Length

2024-05-21T10:16:05.996608image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:06.287780image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
q3 53707
29.4%
q1 50992
28.0%
q2 49714
27.3%
q4 27961
15.3%

Most occurring characters

ValueCountFrequency (%)
Q 182374
50.0%
3 53707
 
14.7%
1 50992
 
14.0%
2 49714
 
13.6%
4 27961
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 182374
50.0%
3 53707
 
14.7%
1 50992
 
14.0%
2 49714
 
13.6%
4 27961
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 182374
50.0%
3 53707
 
14.7%
1 50992
 
14.0%
2 49714
 
13.6%
4 27961
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 182374
50.0%
3 53707
 
14.7%
1 50992
 
14.0%
2 49714
 
13.6%
4 27961
 
7.7%

Region
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing14357
Missing (%)7.9%
Memory size2.8 MiB
West
46309 
South
39364 
North
34229 
East
31761 
NORTH
16354 

Length

Max length5
Median length5
Mean length4.5353446
Min length4

Characters and Unicode

Total characters762015
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH
2nd rowNORTH
3rd rowNORTH
4th rowNORTH
5th rowNORTH

Common Values

ValueCountFrequency (%)
West 46309
25.4%
South 39364
21.6%
North 34229
18.8%
East 31761
17.4%
NORTH 16354
 
9.0%
(Missing) 14357
 
7.9%

Length

2024-05-21T10:16:06.607224image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:06.942698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
north 50583
30.1%
west 46309
27.6%
south 39364
23.4%
east 31761
18.9%

Most occurring characters

ValueCountFrequency (%)
t 151663
19.9%
s 78070
10.2%
o 73593
9.7%
h 73593
9.7%
N 50583
 
6.6%
W 46309
 
6.1%
e 46309
 
6.1%
S 39364
 
5.2%
u 39364
 
5.2%
r 34229
 
4.5%
Other values (6) 128938
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 762015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 151663
19.9%
s 78070
10.2%
o 73593
9.7%
h 73593
9.7%
N 50583
 
6.6%
W 46309
 
6.1%
e 46309
 
6.1%
S 39364
 
5.2%
u 39364
 
5.2%
r 34229
 
4.5%
Other values (6) 128938
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 762015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 151663
19.9%
s 78070
10.2%
o 73593
9.7%
h 73593
9.7%
N 50583
 
6.6%
W 46309
 
6.1%
e 46309
 
6.1%
S 39364
 
5.2%
u 39364
 
5.2%
r 34229
 
4.5%
Other values (6) 128938
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 762015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 151663
19.9%
s 78070
10.2%
o 73593
9.7%
h 73593
9.7%
N 50583
 
6.6%
W 46309
 
6.1%
e 46309
 
6.1%
S 39364
 
5.2%
u 39364
 
5.2%
r 34229
 
4.5%
Other values (6) 128938
16.9%
Distinct335
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2023-04-01 00:00:00
Maximum2024-02-29 00:00:00
2024-05-21T10:16:07.376338image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:16:07.792094image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct157
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:08.312346image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5359865
Min length7

Characters and Unicode

Total characters1556742
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowNFFW14PH1
2nd rowNEFM14PK1
3rd rowFM20PC1
4th rowFM20PC1
5th rowNEFM02PFC
ValueCountFrequency (%)
nffm01pgc 8973
 
4.9%
nffm01cl2 7998
 
4.4%
nfm01dq1 7402
 
4.1%
nffw01cl2 6436
 
3.5%
nffw02pfc 6410
 
3.5%
nefw11pd1 6071
 
3.3%
nfw01dq1 5255
 
2.9%
ngfm08pc1 4943
 
2.7%
nefm11pd1 4879
 
2.7%
nfm05dq1 4744
 
2.6%
Other values (147) 119263
65.4%
2024-05-21T10:16:09.107429image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 296841
19.1%
1 213573
13.7%
N 154510
9.9%
P 133004
8.5%
0 126868
8.1%
M 96710
 
6.2%
W 77677
 
5.0%
D 72884
 
4.7%
C 71983
 
4.6%
2 67987
 
4.4%
Other values (15) 244705
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1556742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 296841
19.1%
1 213573
13.7%
N 154510
9.9%
P 133004
8.5%
0 126868
8.1%
M 96710
 
6.2%
W 77677
 
5.0%
D 72884
 
4.7%
C 71983
 
4.6%
2 67987
 
4.4%
Other values (15) 244705
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1556742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 296841
19.1%
1 213573
13.7%
N 154510
9.9%
P 133004
8.5%
0 126868
8.1%
M 96710
 
6.2%
W 77677
 
5.0%
D 72884
 
4.7%
C 71983
 
4.6%
2 67987
 
4.4%
Other values (15) 244705
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1556742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 296841
19.1%
1 213573
13.7%
N 154510
9.9%
P 133004
8.5%
0 126868
8.1%
M 96710
 
6.2%
W 77677
 
5.0%
D 72884
 
4.7%
C 71983
 
4.6%
2 67987
 
4.4%
Other values (15) 244705
15.7%

Quantity
Real number (ℝ)

SKEWED 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1745808
Minimum-3
Maximum89
Zeros186
Zeros (%)0.1%
Negative534
Negative (%)0.3%
Memory size2.8 MiB
2024-05-21T10:16:09.499915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum89
Range92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96682288
Coefficient of variation (CV)0.82312164
Kurtosis1140.7285
Mean1.1745808
Median Absolute Deviation (MAD)0
Skewness24.456273
Sum214213
Variance0.93474649
MonotonicityNot monotonic
2024-05-21T10:16:09.894069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 160918
88.2%
2 16004
 
8.8%
3 2877
 
1.6%
4 816
 
0.4%
-1 518
 
0.3%
5 328
 
0.2%
0 186
 
0.1%
6 178
 
0.1%
7 99
 
0.1%
8 72
 
< 0.1%
Other values (43) 378
 
0.2%
ValueCountFrequency (%)
-3 2
 
< 0.1%
-2 14
 
< 0.1%
-1 518
 
0.3%
0 186
 
0.1%
1 160918
88.2%
2 16004
 
8.8%
3 2877
 
1.6%
4 816
 
0.4%
5 328
 
0.2%
6 178
 
0.1%
ValueCountFrequency (%)
89 1
< 0.1%
66 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
52 1
< 0.1%
49 2
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
42 1
< 0.1%

Gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
G
102230 
L
71630 
P
 
8174
U
 
340

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182374
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowG
3rd rowL
4th rowL
5th rowG

Common Values

ValueCountFrequency (%)
G 102230
56.1%
L 71630
39.3%
P 8174
 
4.5%
U 340
 
0.2%

Length

2024-05-21T10:16:10.417331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:10.708464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
g 102230
56.1%
l 71630
39.3%
p 8174
 
4.5%
u 340
 
0.2%

Most occurring characters

ValueCountFrequency (%)
G 102230
56.1%
L 71630
39.3%
P 8174
 
4.5%
U 340
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 102230
56.1%
L 71630
39.3%
P 8174
 
4.5%
U 340
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 102230
56.1%
L 71630
39.3%
P 8174
 
4.5%
U 340
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 102230
56.1%
L 71630
39.3%
P 8174
 
4.5%
U 340
 
0.2%

Brand
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
TF
179509 
Tf
 
2520
FP
 
345

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters364748
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTF
2nd rowTF
3rd rowTF
4th rowTF
5th rowTF

Common Values

ValueCountFrequency (%)
TF 179509
98.4%
Tf 2520
 
1.4%
FP 345
 
0.2%

Length

2024-05-21T10:16:11.030520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:11.316139image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
tf 182029
99.8%
fp 345
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 182029
49.9%
F 179854
49.3%
f 2520
 
0.7%
P 345
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 182029
49.9%
F 179854
49.3%
f 2520
 
0.7%
P 345
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 182029
49.9%
F 179854
49.3%
f 2520
 
0.7%
P 345
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 182029
49.9%
F 179854
49.3%
f 2520
 
0.7%
P 345
 
0.1%

Channel
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
SS
88915 
LS
70208 
PT
14569 
CT
 
7917
LL
 
655
Other values (2)
 
110

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters364748
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSS
2nd rowSS
3rd rowSS
4th rowSS
5th rowSS

Common Values

ValueCountFrequency (%)
SS 88915
48.8%
LS 70208
38.5%
PT 14569
 
8.0%
CT 7917
 
4.3%
LL 655
 
0.4%
AZ 69
 
< 0.1%
ss 41
 
< 0.1%

Length

2024-05-21T10:16:11.666198image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:12.013660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ss 88956
48.8%
ls 70208
38.5%
pt 14569
 
8.0%
ct 7917
 
4.3%
ll 655
 
0.4%
az 69
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 248038
68.0%
L 71518
 
19.6%
T 22486
 
6.2%
P 14569
 
4.0%
C 7917
 
2.2%
s 82
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 248038
68.0%
L 71518
 
19.6%
T 22486
 
6.2%
P 14569
 
4.0%
C 7917
 
2.2%
s 82
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 248038
68.0%
L 71518
 
19.6%
T 22486
 
6.2%
P 14569
 
4.0%
C 7917
 
2.2%
s 82
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 248038
68.0%
L 71518
 
19.6%
T 22486
 
6.2%
P 14569
 
4.0%
C 7917
 
2.2%
s 82
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Franchisee store
Text

MISSING 

Distinct342
Distinct (%)0.2%
Missing18119
Missing (%)9.9%
Memory size2.8 MiB
2024-05-21T10:16:12.645873image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1256217
Min length5

Characters and Unicode

Total characters841909
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSS241
2nd rowSS241
3rd rowSS241
4th rowSS241
5th rowSS241
ValueCountFrequency (%)
ss222 3554
 
2.2%
ss223 3194
 
1.9%
ls751 2957
 
1.8%
ls760 2599
 
1.6%
ss255 2214
 
1.3%
ss283 2151
 
1.3%
ss310 1848
 
1.1%
ss225 1816
 
1.1%
ls741 1786
 
1.1%
ss261 1781
 
1.1%
Other values (332) 140355
85.4%
2024-05-21T10:16:13.685501image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 223133
26.5%
2 103464
12.3%
7 80378
 
9.5%
3 68802
 
8.2%
L 63314
 
7.5%
1 62614
 
7.4%
0 53269
 
6.3%
4 32523
 
3.9%
8 31305
 
3.7%
5 30782
 
3.7%
Other values (8) 92325
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 841909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 223133
26.5%
2 103464
12.3%
7 80378
 
9.5%
3 68802
 
8.2%
L 63314
 
7.5%
1 62614
 
7.4%
0 53269
 
6.3%
4 32523
 
3.9%
8 31305
 
3.7%
5 30782
 
3.7%
Other values (8) 92325
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 841909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 223133
26.5%
2 103464
12.3%
7 80378
 
9.5%
3 68802
 
8.2%
L 63314
 
7.5%
1 62614
 
7.4%
0 53269
 
6.3%
4 32523
 
3.9%
8 31305
 
3.7%
5 30782
 
3.7%
Other values (8) 92325
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 841909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 223133
26.5%
2 103464
12.3%
7 80378
 
9.5%
3 68802
 
8.2%
L 63314
 
7.5%
1 62614
 
7.4%
0 53269
 
6.3%
4 32523
 
3.9%
8 31305
 
3.7%
5 30782
 
3.7%
Other values (8) 92325
11.0%
Distinct342
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:14.293136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1232906
Min length5

Characters and Unicode

Total characters934355
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSS241
2nd rowSS241
3rd rowSS241
4th rowSS241
5th rowSS241
ValueCountFrequency (%)
ss222 3958
 
2.2%
ss223 3527
 
1.9%
ls751 3301
 
1.8%
ls760 2879
 
1.6%
ss255 2446
 
1.3%
ss283 2385
 
1.3%
ss310 2025
 
1.1%
ls741 2013
 
1.1%
ss225 2002
 
1.1%
ss261 1988
 
1.1%
Other values (332) 155850
85.5%
2024-05-21T10:16:15.233995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 248120
26.6%
2 114765
12.3%
7 89766
 
9.6%
3 76186
 
8.2%
L 70863
 
7.6%
1 69020
 
7.4%
0 59289
 
6.3%
4 35638
 
3.8%
8 34951
 
3.7%
5 33960
 
3.6%
Other values (8) 101797
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 934355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 248120
26.6%
2 114765
12.3%
7 89766
 
9.6%
3 76186
 
8.2%
L 70863
 
7.6%
1 69020
 
7.4%
0 59289
 
6.3%
4 35638
 
3.8%
8 34951
 
3.7%
5 33960
 
3.6%
Other values (8) 101797
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 934355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 248120
26.6%
2 114765
12.3%
7 89766
 
9.6%
3 76186
 
8.2%
L 70863
 
7.6%
1 69020
 
7.4%
0 59289
 
6.3%
4 35638
 
3.8%
8 34951
 
3.7%
5 33960
 
3.6%
Other values (8) 101797
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 934355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 248120
26.6%
2 114765
12.3%
7 89766
 
9.6%
3 76186
 
8.2%
L 70863
 
7.6%
1 69020
 
7.4%
0 59289
 
6.3%
4 35638
 
3.8%
8 34951
 
3.7%
5 33960
 
3.6%
Other values (8) 101797
10.9%
Distinct339
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:15.713488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length42
Median length34
Mean length21.517234
Min length9

Characters and Unicode

Total characters3924184
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row158-SSL-VIJAYWADA
2nd row158-SSL-VIJAYWADA
3rd row158-SSL-VIJAYWADA
4th row158-SSL-VIJAYWADA
5th row158-SSL-VIJAYWADA
ValueCountFrequency (%)
29582
 
6.5%
city 16315
 
3.6%
mall 13735
 
3.0%
kolkata 13026
 
2.9%
ls 8784
 
1.9%
market 7650
 
1.7%
phoenix 6813
 
1.5%
pune 5192
 
1.1%
center 4519
 
1.0%
lake 4275
 
0.9%
Other values (608) 343989
75.8%
2024-05-21T10:16:16.614495image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 306304
 
7.8%
270524
 
6.9%
S 269350
 
6.9%
A 248796
 
6.3%
L 232503
 
5.9%
a 169490
 
4.3%
T 128762
 
3.3%
R 113002
 
2.9%
I 97571
 
2.5%
O 96572
 
2.5%
Other values (60) 1991310
50.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3924184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 306304
 
7.8%
270524
 
6.9%
S 269350
 
6.9%
A 248796
 
6.3%
L 232503
 
5.9%
a 169490
 
4.3%
T 128762
 
3.3%
R 113002
 
2.9%
I 97571
 
2.5%
O 96572
 
2.5%
Other values (60) 1991310
50.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3924184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 306304
 
7.8%
270524
 
6.9%
S 269350
 
6.9%
A 248796
 
6.3%
L 232503
 
5.9%
a 169490
 
4.3%
T 128762
 
3.3%
R 113002
 
2.9%
I 97571
 
2.5%
O 96572
 
2.5%
Other values (60) 1991310
50.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3924184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 306304
 
7.8%
270524
 
6.9%
S 269350
 
6.9%
A 248796
 
6.3%
L 232503
 
5.9%
a 169490
 
4.3%
T 128762
 
3.3%
R 113002
 
2.9%
I 97571
 
2.5%
O 96572
 
2.5%
Other values (60) 1991310
50.7%

MRP
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731.4531
Minimum395
Maximum4995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:16.921341image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum395
5-th percentile499
Q1645
median1895
Q32595
95-th percentile3095
Maximum4995
Range4600
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation1017.3611
Coefficient of variation (CV)0.58757646
Kurtosis-0.68494272
Mean1731.4531
Median Absolute Deviation (MAD)800
Skewness0.32184595
Sum3.1577202 × 108
Variance1035023.5
MonotonicityNot monotonic
2024-05-21T10:16:17.253432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
499 35794
19.6%
2595 28765
15.8%
645 23548
12.9%
1995 22879
12.5%
1895 13454
 
7.4%
1595 10716
 
5.9%
2695 9727
 
5.3%
3995 6525
 
3.6%
1295 6392
 
3.5%
3095 4997
 
2.7%
Other values (25) 19577
10.7%
ValueCountFrequency (%)
395 515
 
0.3%
399 12
 
< 0.1%
499 35794
19.6%
545 108
 
0.1%
595 406
 
0.2%
645 23548
12.9%
845 2592
 
1.4%
895 355
 
0.2%
995 2661
 
1.5%
1195 5
 
< 0.1%
ValueCountFrequency (%)
4995 282
 
0.2%
4795 587
 
0.3%
4590 206
 
0.1%
4190 41
 
< 0.1%
3995 6525
3.6%
3095 4997
2.7%
2995 4554
2.5%
2895 1270
 
0.7%
2795 4186
2.3%
2695 9727
5.3%

Gross UCP
Real number (ℝ)

Distinct266
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2024.9683
Minimum-7785
Maximum177555
Zeros186
Zeros (%)0.1%
Negative534
Negative (%)0.3%
Memory size2.8 MiB
2024-05-21T10:16:17.633008image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-7785
5-th percentile499
Q1645
median1895
Q32595
95-th percentile4785
Maximum177555
Range185340
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation2048.1348
Coefficient of variation (CV)1.0114405
Kurtosis626.55447
Mean2024.9683
Median Absolute Deviation (MAD)900
Skewness14.545639
Sum3.6930156 × 108
Variance4194856.3
MonotonicityNot monotonic
2024-05-21T10:16:18.010486image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 31814
17.4%
2595 25354
13.9%
645 21095
11.6%
1995 20185
11.1%
1895 11223
 
6.2%
2695 8882
 
4.9%
1595 8066
 
4.4%
3995 5915
 
3.2%
1295 5742
 
3.1%
3095 4672
 
2.6%
Other values (256) 39426
21.6%
ValueCountFrequency (%)
-7785 1
 
< 0.1%
-5590 1
 
< 0.1%
-5190 1
 
< 0.1%
-4995 1
 
< 0.1%
-4990 1
 
< 0.1%
-4795 2
 
< 0.1%
-4785 1
 
< 0.1%
-4590 1
 
< 0.1%
-4190 1
 
< 0.1%
-3995 37
< 0.1%
ValueCountFrequency (%)
177555 1
< 0.1%
119800 1
< 0.1%
105270 1
< 0.1%
95700 1
< 0.1%
92510 1
< 0.1%
90915 1
< 0.1%
87780 1
< 0.1%
82940 1
< 0.1%
78155 2
< 0.1%
71775 1
< 0.1%

Net UCP
Real number (ℝ)

Distinct7439
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1805.9932
Minimum-6785
Maximum125705.09
Zeros447
Zeros (%)0.2%
Negative543
Negative (%)0.3%
Memory size2.8 MiB
2024-05-21T10:16:18.388157image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-6785
5-th percentile499
Q1645
median1795.5
Q32545.75
95-th percentile3995
Maximum125705.09
Range132490.09
Interquartile range (IQR)1900.75

Descriptive statistics

Standard deviation1569.9259
Coefficient of variation (CV)0.86928669
Kurtosis433.99923
Mean1805.9932
Median Absolute Deviation (MAD)800.5
Skewness10.284241
Sum3.2936621 × 108
Variance2464667.2
MonotonicityNot monotonic
2024-05-21T10:16:18.776684image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 26774
 
14.7%
645 16756
 
9.2%
2595 12055
 
6.6%
1995 11151
 
6.1%
1795.5 5758
 
3.2%
1895 5741
 
3.1%
1295 5342
 
2.9%
2695 4322
 
2.4%
2335.5 4279
 
2.3%
1595 3147
 
1.7%
Other values (7429) 87049
47.7%
ValueCountFrequency (%)
-6785 1
 
< 0.1%
-6380 1
 
< 0.1%
-5190 1
 
< 0.1%
-4590 1
 
< 0.1%
-4495 1
 
< 0.1%
-4241.5 1
 
< 0.1%
-4000 1
 
< 0.1%
-3995 20
< 0.1%
-3836 2
 
< 0.1%
-3690 1
 
< 0.1%
ValueCountFrequency (%)
125705.09 1
< 0.1%
99800 1
< 0.1%
73381 1
< 0.1%
65934 1
< 0.1%
59940 1
< 0.1%
57942 1
< 0.1%
56862.07 1
< 0.1%
52242.03 1
< 0.1%
48725.9 1
< 0.1%
48693.79 1
< 0.1%
Distinct139
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:19.199823image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length18
Median length11
Mean length6.925077
Min length3

Characters and Unicode

Total characters1262954
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIJAYAWADA
2nd rowVIJAYAWADA
3rd rowVIJAYAWADA
4th rowVIJAYAWADA
5th rowVIJAYAWADA
ValueCountFrequency (%)
kolkata 22552
 
12.3%
mumbai 22055
 
12.0%
bangalore 19210
 
10.5%
pune 12133
 
6.6%
delhi 11455
 
6.2%
hyderabad 9860
 
5.4%
noida 7457
 
4.1%
chennai 5585
 
3.0%
lucknow 3978
 
2.2%
bhubaneswar 3793
 
2.1%
Other values (85) 65286
35.6%
2024-05-21T10:16:19.971926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 222046
17.6%
o 72862
 
5.8%
e 72847
 
5.8%
i 71512
 
5.7%
n 70419
 
5.6%
r 70247
 
5.6%
u 67066
 
5.3%
l 61451
 
4.9%
d 47145
 
3.7%
h 43268
 
3.4%
Other values (38) 464091
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1262954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 222046
17.6%
o 72862
 
5.8%
e 72847
 
5.8%
i 71512
 
5.7%
n 70419
 
5.6%
r 70247
 
5.6%
u 67066
 
5.3%
l 61451
 
4.9%
d 47145
 
3.7%
h 43268
 
3.4%
Other values (38) 464091
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1262954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 222046
17.6%
o 72862
 
5.8%
e 72847
 
5.8%
i 71512
 
5.7%
n 70419
 
5.6%
r 70247
 
5.6%
u 67066
 
5.3%
l 61451
 
4.9%
d 47145
 
3.7%
h 43268
 
3.4%
Other values (38) 464091
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1262954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 222046
17.6%
o 72862
 
5.8%
e 72847
 
5.8%
i 71512
 
5.7%
n 70419
 
5.6%
r 70247
 
5.6%
u 67066
 
5.3%
l 61451
 
4.9%
d 47145
 
3.7%
h 43268
 
3.4%
Other values (38) 464091
36.7%
Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
MAHARASTRA
39645 
KARNATAKA
22044 
WEST BENGAL
19783 
DELHI
12535 
UTTAR PRADESH
11321 
Other values (32)
77046 

Length

Max length17
Median length15
Mean length9.362623
Min length3

Characters and Unicode

Total characters1707499
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANDHRA PRADESH
2nd rowANDHRA PRADESH
3rd rowANDHRA PRADESH
4th rowANDHRA PRADESH
5th rowANDHRA PRADESH

Common Values

ValueCountFrequency (%)
MAHARASTRA 39645
21.7%
KARNATAKA 22044
12.1%
WEST BENGAL 19783
10.8%
DELHI 12535
 
6.9%
UTTAR PRADESH 11321
 
6.2%
WESTBENGAL 8276
 
4.5%
GUJARAT 8062
 
4.4%
Telangana 7312
 
4.0%
HARYANA 6206
 
3.4%
TAMIL NADU 5530
 
3.0%
Other values (27) 41660
22.8%

Length

2024-05-21T10:16:20.333957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharastra 39645
17.5%
karnataka 22044
 
9.7%
west 19783
 
8.7%
bengal 19783
 
8.7%
pradesh 18637
 
8.2%
delhi 12535
 
5.5%
uttar 11321
 
5.0%
telangana 9886
 
4.4%
westbengal 8276
 
3.6%
gujarat 8062
 
3.6%
Other values (26) 56938
25.1%

Most occurring characters

ValueCountFrequency (%)
A 424038
24.8%
R 173187
10.1%
T 154675
 
9.1%
S 108254
 
6.3%
H 104923
 
6.1%
E 96943
 
5.7%
N 79936
 
4.7%
M 57695
 
3.4%
D 56187
 
3.3%
L 52025
 
3.0%
Other values (29) 399636
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1707499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 424038
24.8%
R 173187
10.1%
T 154675
 
9.1%
S 108254
 
6.3%
H 104923
 
6.1%
E 96943
 
5.7%
N 79936
 
4.7%
M 57695
 
3.4%
D 56187
 
3.3%
L 52025
 
3.0%
Other values (29) 399636
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1707499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 424038
24.8%
R 173187
10.1%
T 154675
 
9.1%
S 108254
 
6.3%
H 104923
 
6.1%
E 96943
 
5.7%
N 79936
 
4.7%
M 57695
 
3.4%
D 56187
 
3.3%
L 52025
 
3.0%
Other values (29) 399636
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1707499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 424038
24.8%
R 173187
10.1%
T 154675
 
9.1%
S 108254
 
6.3%
H 104923
 
6.1%
E 96943
 
5.7%
N 79936
 
4.7%
M 57695
 
3.4%
D 56187
 
3.3%
L 52025
 
3.0%
Other values (29) 399636
23.4%

Channel-2
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
LFS
182374 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters547122
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 182374
100.0%

Length

2024-05-21T10:16:20.648923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:20.936018image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
lfs 182374
100.0%

Most occurring characters

ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Billing grp
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
LFS
182374 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters547122
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 182374
100.0%

Length

2024-05-21T10:16:21.215904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T10:16:21.465209image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
lfs 182374
100.0%

Most occurring characters

ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 547122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 182374
33.3%
F 182374
33.3%
S 182374
33.3%
Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-05-21T10:16:21.803264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length52
Median length38
Mean length15.799769
Min length8

Characters and Unicode

Total characters2881467
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAqua W 30 ml
2nd rowAqua M 90 ml
3rd rowTales M Rio 100 ml
4th rowTales M Rio 100 ml
5th row100 M Steele
ValueCountFrequency (%)
m 95117
 
12.4%
w 73947
 
9.7%
deo 57025
 
7.5%
ml 55172
 
7.2%
100 53526
 
7.0%
150 35806
 
4.7%
raw 31772
 
4.2%
20 29309
 
3.8%
celeste 26988
 
3.5%
50 18549
 
2.4%
Other values (97) 287121
37.6%
2024-05-21T10:16:22.539261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
582335
20.2%
e 284005
 
9.9%
0 211370
 
7.3%
l 130246
 
4.5%
o 129906
 
4.5%
M 118408
 
4.1%
1 96344
 
3.3%
a 89006
 
3.1%
W 79242
 
2.8%
m 77316
 
2.7%
Other values (52) 1083289
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2881467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
582335
20.2%
e 284005
 
9.9%
0 211370
 
7.3%
l 130246
 
4.5%
o 129906
 
4.5%
M 118408
 
4.1%
1 96344
 
3.3%
a 89006
 
3.1%
W 79242
 
2.8%
m 77316
 
2.7%
Other values (52) 1083289
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2881467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
582335
20.2%
e 284005
 
9.9%
0 211370
 
7.3%
l 130246
 
4.5%
o 129906
 
4.5%
M 118408
 
4.1%
1 96344
 
3.3%
a 89006
 
3.1%
W 79242
 
2.8%
m 77316
 
2.7%
Other values (52) 1083289
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2881467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
582335
20.2%
e 284005
 
9.9%
0 211370
 
7.3%
l 130246
 
4.5%
o 129906
 
4.5%
M 118408
 
4.1%
1 96344
 
3.3%
a 89006
 
3.1%
W 79242
 
2.8%
m 77316
 
2.7%
Other values (52) 1083289
37.6%

Collection
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Gift Pack
29563 
Classic 100 ml
29340 
Premium Deo
25533 
Classic 20 ml
24062 
Classic 50 ml
13982 
Other values (13)
59894 

Length

Max length16
Median length14
Mean length11.373589
Min length4

Characters and Unicode

Total characters2074247
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAqua
2nd rowAqua
3rd rowTales 100ml
4th rowTales 100ml
5th rowClassic 100 ml

Common Values

ValueCountFrequency (%)
Gift Pack 29563
16.2%
Classic 100 ml 29340
16.1%
Premium Deo 25533
14.0%
Classic 20 ml 24062
13.2%
Classic 50 ml 13982
7.7%
Aqua 12519
6.9%
Escapade Deo 10273
 
5.6%
Escapade 9795
 
5.4%
Nox 100 ml 6525
 
3.6%
Deo Bundle Pack 6384
 
3.5%
Other values (8) 14398
7.9%

Length

2024-05-21T10:16:22.894270image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ml 79156
18.3%
classic 68654
15.9%
deo 42190
9.8%
pack 35947
8.3%
100 35865
8.3%
gift 29563
 
6.8%
20 29309
 
6.8%
premium 25533
 
5.9%
escapade 20068
 
4.6%
50 13982
 
3.2%
Other values (12) 52356
12.1%

Most occurring characters

ValueCountFrequency (%)
292441
14.1%
a 169324
 
8.2%
l 165503
 
8.0%
s 160258
 
7.7%
m 137742
 
6.6%
0 130057
 
6.3%
c 125015
 
6.0%
i 123782
 
6.0%
e 97968
 
4.7%
C 68654
 
3.3%
Other values (27) 603503
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2074247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
292441
14.1%
a 169324
 
8.2%
l 165503
 
8.0%
s 160258
 
7.7%
m 137742
 
6.6%
0 130057
 
6.3%
c 125015
 
6.0%
i 123782
 
6.0%
e 97968
 
4.7%
C 68654
 
3.3%
Other values (27) 603503
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2074247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
292441
14.1%
a 169324
 
8.2%
l 165503
 
8.0%
s 160258
 
7.7%
m 137742
 
6.6%
0 130057
 
6.3%
c 125015
 
6.0%
i 123782
 
6.0%
e 97968
 
4.7%
C 68654
 
3.3%
Other values (27) 603503
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2074247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
292441
14.1%
a 169324
 
8.2%
l 165503
 
8.0%
s 160258
 
7.7%
m 137742
 
6.6%
0 130057
 
6.3%
c 125015
 
6.0%
i 123782
 
6.0%
e 97968
 
4.7%
C 68654
 
3.3%
Other values (27) 603503
29.1%

Interactions

2024-05-21T10:15:58.945768image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:55.369320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:56.557597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:57.735790image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:59.231403image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:55.681064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:56.852539image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:58.026420image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:59.516427image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:55.947486image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:57.130908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:58.306184image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:59.849106image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:56.249919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:57.426561image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T10:15:58.606385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Missing values

2024-05-21T10:16:00.616241image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-21T10:16:02.019983image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-21T10:16:03.406078image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeChannel-2Billing grpVarient Name2Collection
429152023-242023-04-01AprQ1NORTH2023-04-20 00:00:00NFFW14PH12.0LTFSSSS241SS241158-SSL-VIJAYWADA15953190.03190.0VIJAYAWADAANDHRA PRADESHLFSLFSAqua W 30 mlAqua
429162023-242023-04-01AprQ1NORTH2023-04-21 00:00:00NEFM14PK11.0GTFSSSS241SS241158-SSL-VIJAYWADA24952495.01995.0VIJAYAWADAANDHRA PRADESHLFSLFSAqua M 90 mlAqua
429172023-242023-04-01AprQ1NORTH2023-04-20 00:00:00FM20PC11.0LTFSSSS241SS241158-SSL-VIJAYWADA15951595.01595.0VIJAYAWADAANDHRA PRADESHLFSLFSTales M Rio 100 mlTales 100ml
429182023-242023-04-01AprQ1NORTH2023-04-27 00:00:00FM20PC11.0LTFSSSS241SS241158-SSL-VIJAYWADA15951595.01595.0VIJAYAWADAANDHRA PRADESHLFSLFSTales M Rio 100 mlTales 100ml
429192023-242023-04-01AprQ1NORTH2023-04-22 00:00:00NEFM02PFC1.0GTFSSSS241SS241158-SSL-VIJAYWADA22952295.02295.0VIJAYAWADAANDHRA PRADESHLFSLFS100 M SteeleClassic 100 ml
429202023-242023-04-01AprQ1NORTH2023-04-29 00:00:00FM19PC11.0LTFSSSS241SS241158-SSL-VIJAYWADA15951595.01595.0VIJAYAWADAANDHRA PRADESHLFSLFSTales M Oslo 100 mlTales 100ml
429212023-242023-04-01AprQ1NORTH2023-04-26 00:00:00NFFM01PGC1.0GTFSSSS241SS241158-SSL-VIJAYWADA25952595.02595.0VIJAYAWADAANDHRA PRADESHLFSLFS100 M RawClassic 100 ml
429222023-242023-04-01AprQ1NORTH2023-04-23 00:00:00NFFW04PFC1.0LTFSSSS241SS241158-SSL-VIJAYWADA25952595.02595.0VIJAYAWADAANDHRA PRADESHLFSLFS100 W SheerClassic 100 ml
429232023-242023-04-01AprQ1NORTH2023-04-29 00:00:00NFFW05PG21.0LTFSSSS241SS241158-SSL-VIJAYWADA19951995.01995.0VIJAYAWADAANDHRA PRADESHLFSLFSMini 25 ml W (C,S)Gift Pack
429242023-242023-04-01AprQ1NORTH2023-04-28 00:00:00FW21PC11.0LTFSSSS241SS241158-SSL-VIJAYWADA30953095.03095.0VIJAYAWADAANDHRA PRADESHLFSLFSNoura W Floret 100 mlNoura W 100ml
YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeChannel-2Billing grpVarient Name2Collection
9405222023-242023-02-01FebQ4East2024-02-05 00:00:00FW22PC11.0LTFSSSS292SS292312-SSL-RANCHI30953095.03095.00RanchiJHARKHANDLFSLFSNoura W Iris 100 mlNoura W 100ml
9405232023-242023-02-01FebQ4East2024-02-17 00:00:00FW22PC11.0LTFSSSS292SS292312-SSL-RANCHI30953095.03095.00RanchiJHARKHANDLFSLFSNoura W Iris 100 mlNoura W 100ml
9405242023-242023-02-01FebQ4East2024-02-08 00:00:00NFFW14PK11.0LTFSSSS292SS292312-SSL-RANCHI27952795.02795.00RanchiJHARKHANDLFSLFSAqua W 90 mlAqua
9405252023-242023-02-01FebQ4East2024-02-05 00:00:00NFFW03PFL1.0LTFSSSS292SS292312-SSL-RANCHI18951895.01895.00RanchiJHARKHANDLFSLFS50 W NudeClassic 50 ml
9405262023-242023-02-01FebQ4East2024-02-15 00:00:00NFFW01CL21.0GTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSCoffret Deo W CelesteGift Pack
9405272023-242023-02-01FebQ4East2024-02-12 00:00:00NFFW01CL21.0GTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSCoffret Deo W CelesteGift Pack
9405282023-242023-02-01FebQ4East2024-02-05 00:00:00NFFM01PGL1.0GTFSSSS292SS292312-SSL-RANCHI18951895.01895.00RanchiJHARKHANDLFSLFS50 M RawClassic 50 ml
9405292023-242023-02-01FebQ4East2024-02-11 00:00:00NFFW03PFC1.0LTFSSSS292SS292312-SSL-RANCHI25952595.02122.32RanchiJHARKHANDLFSLFS100 W NudeClassic 100 ml
9405302023-242023-02-01FebQ4East2024-02-21 00:00:00NGFM08PC11.0GTFSSSS292SS292312-SSL-RANCHI26952695.02695.00RanchiJHARKHANDLFSLFS100 M CRoadEscapade
9405312023-242023-02-01FebQ4East2024-02-21 00:00:00NFFP01PG21.0PTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSHis & Her Mini 25 ml (V,S)Gift Pack

Duplicate rows

Most frequently occurring

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeChannel-2Billing grpVarient Name2Collection# duplicates
02023-242023-02-01FebQ4East2024-02-08NFFM01PGC1.0GTFSSSS272SS272257-SSL-CITY CENTER KOLKATA25952595.02595.0KolkataWEST BENGALLFSLFS100 M RawClassic 100 ml2
12023-242023-02-01FebQ4East2024-02-22NFFP01PGFL1.0PTFSSSS283SS283188-SSL-ACROPOLIS KOLKATA29952995.02995.0KolkataWEST BENGALLFSLFSHis & Her 50 ml (R,C)Gift Pack2
22023-242023-02-01FebQ4East2024-02-22NFM03DQ11.0GTFSSSS222SS222115-SSL-SALT LAKE KOLKATA499499.0499.0KolkataWEST BENGALLFSLFS150 M Amalfi Men deoPremium Deo2
32023-242023-02-01FebQ4North2024-02-25NFFP01PG21.0PTFSSSS220SS220130-SSL-GIP NOIDA19951995.01995.0NoidaUTTAR PRADESHLFSLFSHis & Her Mini 25 ml (V,S)Gift Pack2
42023-242023-02-01FebQ4West2024-02-09NFM01DQ11.0GTFSSSS260SS260177-SSL-KALYAN499499.0499.0MumbaiMAHARASTRALFSLFS150 M Raw DeoPremium Deo2
52023-242023-02-01FebQ4West2024-02-09NFW01DQ11.0LTFSSSS204SS204101-SSL-ANDHERI499499.0499.0MumbaiMAHARASTRALFSLFS150 W Celeste DeoPremium Deo2
62023-242023-02-01FebQ4West2024-02-17NFFW03PFC1.0LTFSSSS290SS290273-SSL-AUNDH PUNE25952595.02595.0PuneMAHARASTRALFSLFS100 W NudeClassic 100 ml2
72023-242023-02-01FebQ4West2024-02-24NFM06DQ11.0GTFSSSS261SS261196-SSL-VADODARA499499.0499.0BarodaGUJARATLFSLFSDeo M M Groove 150 mlEscapade Deo2
82023-242023-04-01AprQ1NORTH2023-04-13NEFW13PD11.0LTFSSSS283SS283188-SSL-ACROPOLIS KOLKATA645645.0645.0KOLKATAWESTBENGALLFSLFS20 W SheerClassic 20 ml2
92023-242023-04-01AprQ1NORTH2023-04-18NFW01DQ11.0LTFSSSS222SS222115-SSL-SALT LAKE KOLKATA499499.0499.0KOLKATAWESTBENGALLFSLFS150 W Celeste DeoPremium Deo2